Optimization of culture conditions by Response Surface Methodology and Unstructured kinetic modeling for L-Asparaginase production by Pseudonocardia endophytica VUK-10

Abstract

L-asparaginase is an anti-tumor enzyme and widely accepted as chemotherapeutic agent which has activity against acute lymphoblastic leukemia. The current study targets the production of L-asparaginase by Pseudonocardia endophytica VUK-10 by a statistically designed model. Experiments were performed according to central composite design of RSM with five independent variables such as time, pH, temperature, concentrations of maltose and L-asparagine concentration for optimization. All the five conditions had significant interaction with other variables for the maximum response (L-asparaginase production). Maximum L-asparaginase production was recorded as 7.42 IU/ml slightly higher than the model predicted value of 6.8 IU/ml, from statistical optimization studies. An unstructured kinetic model was proposed to depict the profiles of biomass, substrate utilization and L-asparaginase production in optimized medium under shake flask level. The logistic and Leudeking-Piret expressions were modified to predict the kinetic model parameters (µmax, X0, Xmax, α, β, γ and η) and we found that L-asparaginase production was growth-associated. High significant correlation (R 2 ) values of 0.86, 0.96 and 0.94 were observed with the experimental and predicted results for Pseudonocardia endophytica VUK-10 growth, L-asparaginase activity and Maltose utilization, respectively. The results obtained from medium optimization using RSM and unstructured mathematical models describe the L-asparaginase fermentation kinetics more effectively.

Development of L-Asparaginase Biobetters: Current Research Status and Review of the Desirable Quality Profiles

“…The screening of the major variables affecting the process can be carried out using methods such as the Plackett–Burman experimental design or the Taguchi's method (Placket and Brurman, 1946; Rao et al, 2008). Afterwards, in order to find optimal operating conditions, central composite design and the response surface methodology can be used (Managamuri et al, 2017). Other methods, such as model-based optimization are also popular (Zuo et al, 2014).…”

Production, characterization, and applications of a novel thermo-acidophilic L-asparaginase of Pseudomonas aeruginosa CSPS4

“…Similarly, a potential anticancer activity was reported from Lasparaginase of the P. aeruginosa strain. Several Gram-negative bacteria have been shown to produce L-asparaginase activity (Kamble et al 2012;Managamuri et al 2017;Brumano et al 2019). However, most of the Pseudomonas-derived L-asparaginase also exhibit L-glutaminase activity (Brumano et al 2019;Kumar et al 2022).…”

Optimization of L-asparaginase from Enterobacter aesburiae via response surface approach

“…For the study,11 different media (M1, M2,........M11) were used for media optimization, to study their effect on enzyme activity (Table 1) (Wraragkar and Khobragade2009). Response Surface Methodology(RSM) is a statistical modeling and optimization method to study the combined and interactive effects of a given quantitative data using appropriate experimental designs for determining and drawing solution to multivalent equations, that provides an optimum solution achieving maximum output (Managamuri et al 2017).RSM was employed to study the interaction between temperature, rpm and inoculum size in optimized M9 medium using Minitab14 to identify optimized conditions required for enzyme production.15 experiments were conducted for the identi cation of best combinations of three different parameters to obtain maximum enzyme activity (Table 2). The major in uential factors contributing to greater than 50% of total L-asparaginase production are inoculum level and temperature.…”